- html - 出于某种原因,IE8 对我的 Sass 文件中继承的 html5 CSS 不友好?
- JMeter 在响应断言中使用 span 标签的问题
- html - 在 :hover and :active? 上具有不同效果的 CSS 动画
- html - 相对于居中的 html 内容固定的 CSS 重复背景?
我尝试将我的 pytorch Resnet50 模型转换为 ONNX 并进行推理。转换程序没有错误,但是onnx模型的最终结果来自onnxruntime与pytorch的origin模型结果有较大差距。
可能的解决方案是什么?
ONNX 版本:1.5.0
pytorch版本:1.1.0
CUDA:9.0
系统:Ubuntu 18.06
Python:3.5
这里是转换代码
import torch
import models
from collections import OrderedDict
state_dict = "/home/yx-wan/newhome/workspace/filter-pruning-geometric-median/scripts/snapshots/resnet50-rate-0.7/best.resnet50.GM_0.7_76.82.pth.tar"
arch = 'resnet50'
def import_sparse(model,state_dict):
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
print("sparse_model_loaded")
return model
# initialize model
model = models.__dict__[arch](pretrained=False).cuda()
checkpoint = torch.load(state_dict)
model = import_sparse(model, checkpoint['state_dict'])
print("Top 1 precise of model: {}".format(checkpoint['best_prec1']))
dummy_input =torch.randn(1, 3, 224, 224).cuda()
torch.onnx.export(model, dummy_input, "{}.onnx".format(arch), verbose=True)
这是结果检查代码
import sys
from onnxruntime.datasets import get_example
import onnxruntime
import cv2
import numpy as np
import torch
import models
import onnxruntime
from collections import OrderedDict
from my_tools import resize_img
def import_sparse(model,checkpoint):
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
return model
image_path = "./img652.jpg"
onnx_model_path = "/workplace/workspace/filter-pruning-geometric-median/resnet50.onnx"
ckpt="./scripts/snapshots/resnet50-rate-0.7/best.resnet50.GM_0.7_76.82.pth.tar"
img_ori = cv2.imread(image_path) # BGR
img = cv2.cvtColor(img_ori, cv2.COLOR_BGR2RGB)
img, ratio_h, ratio_w = resize_img(img,224,224)
img = img - np.array([123.68, 116.78, 103.94],dtype=np.float32)
img_batch = np.expand_dims(img, 0)
# NHWC -> NCHW
img_batch = np.transpose(img_batch,[0,3,1,2])
example_model = get_example(onnx_model_path)
sess = onnxruntime.InferenceSession(example_model)
input_name = sess.get_inputs()[0].name
print("Input name :", input_name)
input_shape = sess.get_inputs()[0].shape
print("Input shape :", input_shape)
input_type = sess.get_inputs()[0].type
print("Input type :", input_type)
output_name = sess.get_outputs()[0].name
print("Output name :", output_name)
output_shape = sess.get_outputs()[0].shape
print("Output shape :", output_shape)
output_type = sess.get_outputs()[0].type
print("Output type :", output_type)
print("Input data shape{}".format(img_batch.shape))
assert(list(input_shape) == list(img_batch.shape))
result_onnx = sess.run([output_name], {input_name: img_batch})
# initialize model
model = models.__dict__["resnet50"]()
checkpoint = torch.load(ckpt,map_location='cpu')
best_prec1 = checkpoint['best_prec1']
model = import_sparse(model,checkpoint)
img_batch = torch.FloatTensor(img_batch)
with torch.no_grad():
result_torch = model(img_batch)
result_torch = result_torch.numpy()
print("max onnx-torch:{}".format(np.max(result_onnx-result_torch)))
检查代码的输出(带有一些警告,但我认为这并不重要)是
2019-08-09 02:59:21.378599853 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.2.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378654931 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.2.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378665235 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.2.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378675069 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.1.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378686874 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378698995 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378718700 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.5.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378729567 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.4.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378739657 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.4.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378752091 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.3.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378762533 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.3.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378771168 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.2.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378781705 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.2.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378792325 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.4.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378802071 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.1.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378812061 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.0.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378822884 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378834198 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378845176 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.2.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378859324 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378869709 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378883281 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.5.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378893302 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.3.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378904876 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378915507 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.0.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378926638 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378938115 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378948686 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378958670 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.2.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378969125 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378979556 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.378990553 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.2.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379001126 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.2.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379011508 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379021900 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379033504 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.2.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379044076 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.2.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379064049 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379076654 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.0.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379089769 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.1.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379102140 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.0.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379114598 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.3.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379133520 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.2.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379144015 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.3.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379155771 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.1.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379167084 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.3.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379178303 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.0.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379189605 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer4.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379199974 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.1.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379211042 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.0.downsample.1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379221800 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer3.5.bn2.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379232566 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer1.0.bn1.num_batches_tracked'. It is not used by any node and should be removed from the model.
2019-08-09 02:59:21.379243442 [W:onnxruntime:Default, graph.cc:2263 CleanUnusedInitializers] Removing initializer 'layer2.1.bn3.num_batches_tracked'. It is not used by any node and should be removed from the model.
Input name : 0
Input shape : [1, 3, 224, 224]
Input type : tensor(float)
Output name : 503
Output shape : [1, 1000]
Output type : tensor(float)
Input data shape(1, 3, 224, 224)
max onnx-torch:104.89282989501953
最佳答案
通过在测试代码中运行 pytorch 模型推理之前添加 model.eval() 来解决问题。解决方案是 from the link
model = models.__dict__["resnet50"]()
checkpoint = torch.load(ckpt,map_location='cpu')
best_prec1 = checkpoint['best_prec1']
model = import_sparse(model,checkpoint)
model.eval()
img_batch = torch.FloatTensor(img_batch)
with torch.no_grad():
result_torch = model(img_batch)
result_torch = result_torch.numpy()
关于pytorch - ONNX 和 pytorch 的输出不同,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57423150/
我有 table 像这样 -------------------------------------------- id size title priority
我的应用在不同的 Activity (4 个 Activity )中仅包含横幅广告。所以我的疑问是, 我可以对所有横幅广告使用一个广告单元 ID 吗? 或者 每个 Activity 使用不同的广告单元
我有任意(但统一)数字列表的任意列表。 (它们是 n 空间中 bin 的边界坐标,我想绘制其角,但这并不重要。)我想生成所有可能组合的列表。所以:[[1,2], [3,4],[5,6]] 产生 [[1
我刚刚在学校开始学习 Java,正在尝试自定义控件和图形。我目前正在研究图案锁,一开始一切都很好,但突然间它绘制不正确。我确实更改了一些代码,但是当我看到错误时,我立即将其更改回来(撤消,ftw),但
在获取 Distinct 的 Count 时,我在使用 Group By With Rollup 时遇到了一个小问题。 问题是 Rollup 摘要只是所有分组中 Distinct 值的总数,而不是所有
这不起作用: select count(distinct colA, colB) from mytable 我知道我可以通过双选来简单地解决这个问题。 select count(*) from (
这个问题在这里已经有了答案: JavaScript regex whitespace characters (5 个回答) 2年前关闭。 你能解释一下为什么我会得到 false比较 text ===
这个问题已经有答案了: 奥 git _a (56 个回答) 已关闭 9 年前。 我被要求用 Javascript 编写一个函数 sortByFoo 来正确响应此测试: // Does not cras
所以,我不得不说,SQL 是迄今为止我作为开发人员最薄弱的一面。也许我想要完成的事情很简单。我有这样的东西(这不是真正的模型,但为了使其易于理解而不浪费太多时间解释它,我想出了一个完全模仿我必须使用的
这个问题在这里已经有了答案: How does the "this" keyword work? (22 个回答) 3年前关闭。 简而言之:为什么在使用 Objects 时,直接调用的函数和通过引用传
这个问题在这里已经有了答案: 关闭 12 年前。 Possible Duplicate: what is the difference between (.) dot operator and (-
我真的不明白这里发生了什么但是: 当我这样做时: colorIndex += len - stopPos; for(int m = 0; m < len - stopPos; m++) { c
思考 MySQL 中的 Group By 函数的最佳方式是什么? 我正在编写一个 MySQL 查询,通过 ODBC 连接在 Excel 的数据透视表中提取数据,以便用户可以轻松访问数据。 例如,我有:
我想要的SQL是这样的: SELECT week_no, type, SELECT count(distinct user_id) FROM group WHERE pts > 0 FROM bas
商店表: +--+-------+--------+ |id|name |date | +--+-------+--------+ |1 |x |Ma
对于 chrome 和 ff,当涉及到可怕的 ie 时,这个脚本工作完美。有问题 function getY(oElement) { var curtop = 0; if (oElem
我现在无法提供代码,因为我目前正在脑海中研究这个想法并在互联网上四处乱逛。 我了解了进程间通信和使用共享内存在进程之间共享数据(特别是结构)。 但是,在对保存在不同 .c 文件中的程序使用 fork(
我想在用户集合中使用不同的功能。在 mongo shell 中,我可以像下面这样使用: db.users.distinct("name"); 其中名称是用于区分的集合字段。 同样我想要,在 C
List nastava_izvjestaj = new List(); var data_context = new DataEvidencijaDataContext();
我的 Rails 应用程序中有 Ransack 搜索和 Foundation,本地 css 渲染正常,而生产中的同一个应用程序有一个怪癖: 应用程序中的其他内容完全相同。 我在 Chrome 和 Sa
我是一名优秀的程序员,十分优秀!